Abhishek Kumar Tripathi

Performance metrics for image contrast

In this paper, contrast level of the images are quantified by the two proposed metrics. These metrics are Histogram Flatness Measure (HFM) and Histogram Spread (HS). Computation of these metrics is based on the shape of the histogram. Extensive simulation results reveal that HS is more meaningful than HFM. Low contrast images have low HS value, while high contrast images have higher value of HS. Thus HS metric can be used to distinguish between the images having different contrast level. Accuracy of the metric is also verified for natural and medical images. This metric has broad applications in image retrieval, image database management, visualization, rendering and image classification.

Removal of Fog from Images: A Review

Abstract In this paper, reported algorithms for the removal of fog are reviewed. Fog reduces the visibility of scene and thus performance of various computer vision algorithms which use feature information. Formation of fog is the function of the depth. Estimation of depth information is under constraint problem if single image is available. Hence, removal of fog requires assumptions or prior information. Fog removal algorithms estimate the depth information with various assumptions, which are discussed in detail here. Fog removal algorithm has a wide application in tracking and navigation, consumer electronics, and entertainment industries.

Single image fog removal using bilateral filter

In this paper, a novel and efficient fog removal algorithm is proposed. Fog formation is due to attenuation and airlight. Attenuation reduces the contrast and airlight increases the whiteness in the scene. Proposed algorithm uses bilateral filter for the estimation of airlight and recover scene contrast. Qualitative and quantitative analysis demonstrate that proposed algorithm performs well in comparison with prior state of the art algorithms. Proposed algorithm is independent of the density of fog and does not require user intervention. It can handle color as well as gray images. Proposed algorithm has a wide application in tracking and navigation, consumer electronics and entertainment industries.

A Probabilistic Approach for Detection and Removal of Rain from Videos

Abstract A novel, efficient, simple, and probabilistic model based rain removal algorithm is proposed in this paper. Proposed algorithm is robust to the rain intensity variations. Probabilistic approach automatically adjust the threshold and effectively discriminate the rain pixels and non-rain moving object pixels. Discrimination between the rain and non-rain moving objects is based on the time evolution of pixels in consecutive frames. Proposed algorithm does not assume the shape, size and velocity of the raindrops and intensity of rain, which makes it robust to different rain conditions. For performance evaluation, in addition to miss and false detection a new metric temporal variance is introduced. Results show that proposed algorithm outperforms the other rain removal algorithms. Instead of working on all three color components, proposed algorithm works only on the intensity plane. Use of single plane reduces the complexity and execution time of the algorithm.

Removal of rain from videos: a review

In this paper, the algorithms for the detection and removal of rain from videos have been reviewed. Rain reduces the visibility of scene and thus performance of computer vision algorithms which use feature information. Detection and removal of rain requires the discrimination of rain and nonrain pixels. Accuracy of the algorithm depends upon this discrimination. Here merits and demerits of the algorithms are discussed, which motivate the further research. A rain removal algorithm has a wide application in tracking and navigation, consumer electronics and entertainment industries.

Meteorological approach for detection and removal of rain from videos

Rain removal algorithms require certain number of consecutive frames for reducing the visibility of rain from videos. These algorithms are designed for the rain videos captured by the fixed camera. In this study, it is demonstrated that using global motion compensation all the rain removal algorithms developed for fixed camera can be used for moving camera too. In this study a novel, efficient and simple algorithm for detection and removal of rain from video using meteorological properties is proposed. Here meteorological properties are used to separate the rain pixels from the non-rain pixels. The proposed algorithm achieves good accuracy in spite of less number of consecutive frames, which reduces the buffer size and delay. It works only on the intensity plane that further reduces the complexity and execution time significantly. Quantitative and qualitative analyses show that the proposed algorithm removes rain effectively under constrained buffer size and delay in comparison with most of the competing rain removal algorithms.

A new adaptive median filtering technique for removal of impulse noise from images

This paper describes a new technique for the design of Adaptive Median Filtering Technique, aimed at removing the impulse noise (salt and pepper noise) from the image and reducing distortion in the image. The filter is designed to reconfigure itself and provide real-time noise reduction. This paper investigates a high-speed non-linear Adaptive median filter implementation. Then Adaptive Median Filter serves the dual purpose of removing the impulse noise from the image and reducing distortion in the image. Adaptive Median Filtering can achieve the filtering operation of an image corrupted with impulse noise up to 70%.

Efficient fog removal from video

In this paper, a framework of real-time video processing for fog removal using uncalibrated single camera system is proposed. Intelligent use of temporal redundancy present in video frames paves the way for real-time implementation. Any fog removal algorithm for images acquired with uncalibrated single camera system can be extended to video using the proposed framework. For the purpose of real-time implementation, several fog removal algorithms for images are investigated and few top ranking algorithms in speed and quality are chosen. Simulation results confirm that proposed framework reduces the computation per frame significantly. Proposed fog removal framework has a wide application in navigation, transportation, and other industries.

Combating Bad Weather Part I:Rain Removal from Video

Abstract Download Free Sample Current vision systems are designed to perform in normal weather condition. However, no one can escape from severe weather conditions. Bad weather reduces scene contrast and visibility, which results in degradation in the performance of various computer vision algorithms such as object tracking, segmentation and recognition. Thus, current vision systems must include some mechanisms that enable them to perform up to the mark in bad weather conditions such as rain and fog. Rain causes the spatial and temporal intensity variations in images or video frames. These intensity changes are due to the random distribution and high velocities of the raindrops. Fog causes low contrast and whiteness in the image and leads to a shift in the color. This book has studied rain and fog from the perspective of vision. The book has two main goals: 1) removal of rain from videos captured by a moving and static camera, 2) removal of the fog from images and videos captured by a moving single uncali...